Adaptive graph attention-guided parallel sampling and embedded selection for multi-model fitting
Abstract
Multi-model fitting is a fundamental challenge in computer vision, where real-world data often contains severe gross outliers and pseudo-outliers. Existing methods rely on inefficient sequential hypothesize-and-verify frameworks that require a predefined number of models and inlier thresholds-parameters that are difficult to determine in practical scenes. To overcome these limitations, we propose a novel Adaptive Graph Attention-guided parallel multi-model fitting method (AGASAC) that jointly learns local and global features, performs parallel hypothesis sampling, and executes confidence-embedded model selection. Specifically, we design a dual-confidence graph attention module that models data relationships using an adaptive graph attention network. This module computes minimal-set confidence and quality confidence to guide the multi-model fitting process, eliminating manual parameter tuning. Additionally, we propose a parallel discriminative sampling module that leverages minimal-set confidence to concurrently sample hypotheses. By enforcing a quantized consensus constraint, this module maximizes inter-model variance while minimizing intra-model discrepancy. It enables computationally efficient hypothesis generation and pseudo-outlier suppression. To obtain high-quality models, we present a quality-embedded selection module that integrates quality confidence into the joint optimization of model selection and data clustering. Extensive experiments show that the proposed method achieves a lower transfer error of 0.39 pixels and a 36.92% runtime reduction, surpassing state-of-the-art methods. The code is available at https://github.com/YWY-Vivian/AGASAC.
Document Type
Conference Proceeding
Date of Publication
10-27-2025
Publication Title
MM 2025 Proceedings of the 33rd ACM International Conference on Multimedia Co Located with MM 2025
Publisher
Association for Computing Machinery
School
School of Science
Funders
National Natural Science Foundation of China (U21A20514, 62476112) / Australian Research Council / Guangdong Basic and Applied Basic Research Foundation (2024A1515011740)
Grant Number
ARC Number : DP250104390
Copyright
free_to_read
First Page
10896
Last Page
10904
Comments
Yin, W., Lin, S., Suter, D., & Wang, H. (2025). Adaptive graph attention-guided parallel sampling and embedded selection for multi-model fitting. In Proceedings of the 33rd ACM International Conference on Multimedia (pp. 10896-10904). https://doi.org/10.1145/3746027.3755050